Organizations will be able to develop and deploy Apache Kafka applications without the need to lock in their data with Glassbeam.
Machine data analytics providerGlassbeam, Inc. announced it is upgrading its IIoT analytics platform with the addition of Apache Kafka integration to provide an open source stream processing model. The upgrade will allow organizations to develop and deploy custom machine data analytics apps without having to lock their data in with Glassbeam.
The company says it hopes the enhanced platform will help enterprises to decide whether to build their own platform or invest in a third party one. The platform’s Semiotic Parsing Language (SPL) provides a data transformation and preparation framework for complex machine data. With the addition of Apache Kafka, the platform will now allow customers to connect to any data store in addition to deploying the platform on-site.
“Developers anywhere can now collect and enrich machine data from any source in their organization. With open access to our core platform, developers can use their existing enterprise apps, connectors, and tools to deploy Kafka-based parsed data quickly,” says Puneet Pandit, co-founder and CEO of Glassbeam. “With our open platform, organizations now have the complete freedom to build custom connected-machine applications with Glassbeam without the need to build their own data ingestion platform that may not fulfill their business objectives.”
According to the company’s announcement, key features include access to open standards, improved developer productivity, elimination of unnecessary data preparation burden, better focus on organizational core, and re-use of existing infrastructure investment.
The Glassbeam platform with the new integration with Apache Kafka is now available with Glassbeam 5.7 as an on-site managed service. The pricing varies and is based on retention periods and data processed per day.
SANTA CLARA, Calif., Feb. 15, 2018 /PRNewswire/ -- Glassbeam, Inc., the premier machine data analytics company, announced today that it has successfully built Artificial Intelligence (AI) applications powered by Machine Learning (ML) models for predicting part failures in expensive imaging modalities, allowing healthcare providers to deliver better and more efficient patient care. Business impact of such new applications delivered real time through cloud-based dashboards and rules-based alerts will revolutionize the landscape on how equipment maintenance is performed today by in-house support staff at healthcare providers, independent service organizations (ISOs), and by the OEMs themselves.
"The management of medical machines such as MRI and CT Scanners have taken on a new level of complexity in recent years, due in part to the increased sophistication of equipment and ever-increasing requirements for compliance, safety, reliability and accuracy," said Corey Holtman, President at Gateway Diagnostic Imaging. "Predicting machine health and utilization patterns with help from latest techniques of Artificial Intelligence and Machine Learning is the next frontier to improve operations in Clinical Engineering function. I am pleased to see Glassbeam innovating on this exciting front for healthcare providers."
The first phase of these applications will focus on CAT (Computed Tomography) Scanners that can cost anywhere between $1 million to $2.5 million or more, depending upon the desired image quality in procedures such as CT Angiography (CTA). One of the most expensive parts of a typical CAT Scanner is the X-ray tube provided by OEMs costing anywhere between $150,000 to $200,000. Unfortunately, replacing a CT scanner's X-ray tube today is more an art than a science and is based on a number of ad hoc data inputs based on age of the machine, number of scans performed, image quality rendered amongst other subjective factors. Without proper diagnostics on machine data signals, many companies end up replacing tubes under the gun to ensure machine uptime at all costs. Glassbeam now has the solution allowing a facility to get a warning signal about a week in advance of a potential tube failure. This can alert the clinical engineering staff to become proactive in avoiding unplanned downtime, saving costs, and averting patient re-scheduling at the last minute.
"The number of signals coming from connected machines in the IoT market have surpassed the ability for humans to keep track of them years ago," said Lise Getoor, Professor of Computer Science and Center Director of D3 (Data, Discovery and Decisions) initiative at University of California, Santa Cruz. "I am excited to see Glassbeam, as a supporting member of D3 Center, taking a leadership role in leveraging artificial intelligence to change the rules of the game for the healthcare market."
"The parts replacement industry for global installed base of medical imaging equipment in 2020 is slated to be a $3.6 billion market," said Puneet Pandit, Co-founder and CEO at Glassbeam. "With AI and ML applications based on analyzing millions of sensor readings captured in Glassbeam cloud each day, even with a modest 10% savings, we are ready to make a significant dent on the underlying inefficiencies of support operations, supply chain, parts and material logistics planning for large enterprises in the healthcare market."
Pricing & Availability
Glassbeam plans to roll out new AI powered dashboards bundled into the current pricing model of its Clinical Engineering Analytics (CLEAN™) IIoT blueprint. For more details, contact email@example.com.
Glassbeam Healthcare IIoT Blueprint: Clinical Engineering Analytics – CLEAN™ Blueprint
Visit us for the latest solution demo at Booth # 313 at healthcare industry show ICE 2018 in Las Vegas, February 16-18, 2018.
Glassbeam is the premier machine data analytics company bringing structure and meaning to complex data generated from any connected machine in the Industrial IoT industry. Funded by several ultra-high net worth investors, Glassbeam's next generation cloud-based platform is designed to transform and analyze multi-structured data, delivering powerful solutions on customer support and product intelligence for companies such as IBM, Dell EMC, Novant Health, and Dimension Data. For more information visit http://www.glassbeam.com or follow us on Twitter@Glassbeam.
Head of Marketing
A new AI system could help hospitals keep their expensive medical equipment healthy by scanning for problems before they become expensive to fix.
Data analytics company Glassbeam has announced a series of new AI applications that will help healthcare providers to identify parts failures in hospital MRI and CT scanners.
Using the new systems, doctors and other medical professionals can tap into machine learning algorithms to ensure that expensive, life-saving systems are kept in constant working order. The applications also offer cloud-based dashboards and alerts to transform the equipment maintenance process.
Glassbeam said it wants to help healthcare organisations “deliver better and more efficient patient care”.
It can be an expensive and lengthy process for medical technicians to take systems offline and repair them internally. Meanwhile, predicting or planning for failures can be a challenge in environments where investment is tight, time is critical, and lives are at stake.
Corey Holtman, president at Gateway Diagnostic Imaging, said medical imaging systems are becoming increasingly complex. As a result, AI and IoT systems could revolutionise the healthcare sector, he said.
“The management of medical machines such as MRI and CT scanners has taken on a new level of complexity in recent years, due in part to the increased sophistication of equipment and the ever-increasing requirements for compliance, safety, reliability, and accuracy,” he said.
“Predicting machine health and utilisation patterns – with help from the latest techniques in artificial intelligence and machine learning – is the next frontier to improve operations in clinical engineering functions.”
Changing the industry
Glassbeam plans to develop the new systems in phases. The first of these will focus on CT scanners, which are used to create cross-sectional views inside the human body without the need for surgery.
CT systems can cost up to $2.5 million apiece, and when they malfunction or need parts replacing, healthcare providers risk running up six-figure bills, while taking life-saving systems offline.
Glassbeam said that hospitals and clinics are missing out on the predictive capabilities offered by machine learning and big data analytics.
Its new system can warn professionals about problems a week before they might occur. This “can alert clinical engineering staff to become proactive in avoiding unplanned downtime, saving costs, and averting patient re-scheduling at the last minute”, said the company.
Counting the benefits
“The parts replacement industry for the global installed base of medical imaging equipment is slated to be a $3.6 billion market in 2020,” said Puneet Pandit, co-founder and CEO of Glassbeam.
“With AI and ML applications based on analysing millions of sensor readings captured in the Glassbeam cloud each day, even with a modest 10 percent savings we are ready to make a significant dent on the underlying inefficiencies.”
Lise Getoor, professor of computer science at the University of California in Santa Cruz, praised the work being done by Glassbeam, which is a technology partner at UCSC’s D3 (Data, Discovery, Decisions) Center.
“The number of signals coming from connected machines in the IoT market surpassed the ability of humans to keep track of them years ago,” she said.
“I’m excited to see Glassbeam taking a leadership role in leveraging artificial intelligence to change the rules of the game for the healthcare market.”
Internet of Business says
AI’s predictive capabilities, together with technologies such as big data analytics, digital twins, and enterprise asset management (EAM) systems, could be transformative across many sectors as the IoT spreads.
CERN’s Large Hadron Collider, the largest machine ever built, is perhaps the leading example of the technology’s potential. Every single component in the CERN campus is logged in an EAM system as a digital twin, and predictive analytics help engineers predict failures and plan downtime for essential maintenance.
Just as important, the system tells them exactly where the problem lies: an important factor in large, complex systems. With lives at stake as well as big science, these connected technologies have significant potential.
SANTA CLARA, Calif., April 11, 2018 /PRNewswire/ -- Glassbeam, Inc., the premier machine data analytics company, announced today that UCSF Health (University of California, San Francisco) has selected Glassbeam to drive their clinical engineering analytics program. These innovations are aimed at revolutionizing the quality, consistency and efficiency of medical equipment in support of patient care. Glassbeam will collaborate with UCSF to not only deliver value from its proven CLEAN™ blueprint (Clinical Engineering Analytics) to manage major parts of the imaging fleet, but will also expand the solution to various other modalities such as Ultrasound, Cath Lab, and Physiological monitoring equipment.
UCSF Medical Center in San Francisco was ranked number five in the nation by US News & World Report in 2017, with national rankings notched in 15 adult specialties and 9 children's specialties. It also achieved the highest rating possible in 8 procedures or conditions. As one of the pioneers exclusively focused on health, UCSF is driven by the idea that when the best research, the best education and the best patient care converge, great breakthroughs are achieved. This animating idea is driving UCSF Health to partner with Glassbeam in a rapidly changing and evolving health care environment in which UCSF is bringing innovative solutions to meet the growing needs of its patients and the communities it serves.
"Information Services and Analytics fits well within UCSF Health's 2020 strategic goals," said Ramana Sastry, Director of Clinical Engineering at UCSF Health. "Investing in data systems and predictive analytics capabilities to help us facilitate service management, asset utilization and performance improvement of medical machines is critical to our success. We are confident Glassbeam's unique analytics solution will help us tremendously as we scale our operations over next few years."
"The next steps to fulfill the promise of patient care has to include technical and customer service efficiencies in the form of predictive maintenance and machine learning intelligence," said Frank Beltré, Founder and Service Operations Consultant of QDC Biomedical, LLC. "Glassbeam's CLEAN provides machine learning to assist in managing equipment service within a sustainable cost-containment service delivery model. Injecting machine learning into medical equipment service operations will additionally enhance patient care by increasing equipment reliability and availability for medical diagnosis."
"The health care industry is eager to adopt new cutting-edge solutions that bring the rigor and openness of machine data analytics to the world of imaging and bio medical equipment," said Puneet Pandit, Co-founder and CEO at Glassbeam. "Glassbeam is at the forefront of this disruption. We are thrilled to partner with UCSF Health in providing innovative, high-quality, cost-competitive clinical services, and delivering for them an unparalleled patient experience across the entire care continuum."
Glassbeam is the premier machine data analytics company bringing structure and meaning to complex data generated from any connected machine in the Industrial IoT industry. Funded by several ultra-high net worth investors, Glassbeam's next generation cloud-based platform is designed to transform and analyze multi-structured data, delivering powerful solutions on customer support and product intelligence for companies such as IBM, Dell EMC, Novant Health, and Dimension Data. For more information visit http://www.glassbeam.com or follow us on Twitter @Glassbeam.
C3DNA Inc., a cognitive computing and communications company appointed Max Michaels as its President and Chief Executive Officer; also elected him to its Board.Max brings over 20 years of senior leadership experience in information and communication technologies to the Company. He joined C3DNA from IBM Network Services where as the global General Manager he spearheaded offerings based on software-defined infrastructure and services platform with Watson. Previously he held executive roles at AT&T and Cisco spanning corporate strategy, business development and sales operations.
“Max understands the disruptive technologies reshaping the markets and the evolving needs of telecoms and enterprise customers, and he will ensure that C3DNA is well positioned for the cognitive era,” said Kumar Malavalli, Chairman of the company and the Co-founder of Brocade. “Max’s business acumen and impactful leadership experiences make him perfectly suited to lead C3DNA.”
“The technologies developed by the talented team at C3DNA are truly transformational. My priorities are to perfect the use cases for cognitive cloud migration, deliver focused offerings in application networking, and develop a robust roadmap for the next wave of innovations,” said Max Michaels.
About C3DNA Inc.
C3DNA seeks to mobilize global enterprises for a new era of cognitive computing and communications through distributed network architectures.Since 2012 the company has been pioneering breakthroughs in distributed computing, resilient networks and sentient clouds - the building blocks for autonomous application networks. The growing patent portfolio and use cases reflect its thought leadership and advanced capabilities in the rapidly converging information and communication industries.
For more information, visit www.c3dna.com
Thu12Apr2018By Fred Bazzoli
Healthcare organizations spend millions of dollars on imaging devices, so ensuring that they’re optimally maintained is essential in maximizing the return on that investment. Now, predictive analytics and machine learning are being used to do that.
UCSF Medical Center in San Francisco is turning to an information services and analytics product from Glassbeam to power its clinical engineering analytics program.
The hospital will work with the Santa Clara, Calif.-based company to use its CLEAN blueprint (Clinical Engineering Analytics) to manage components of its imaging equipment, with plans to expand its use to other modalities, such as ultrasound, cath lab and physiological monitoring equipment.
“Investing in data systems and predictive analytics capabilities to help us facilitate service management, asset utilization and performance improvement of medical machines is critical to our success,” says Ramana Sastry, director of clinical engineering at UCSF Health. “Glassbeam’s unique analytics solution will help us as we scale our operations over next few years.”
UCSF executives say that imaging medical equipment systems are based on complex technologies, and they increasingly are producing complex machine data that require more advanced data transformation solutions to enable root cause analysis, predictive analytics, machine learning and other high-value support applications.
The Glassbeam technology is intended to help organizations realize value from their machine data, and it can be used to optimize uptime on a variety of devices from different manufacturers, analyzing data to give a better view of operations and provide actionable intelligence.
Using machine learning to improve imaging device performance is a crucial next step, says Frank Beltre, a service operations management consultant for UCSF. “This process traditionally has been done manually, and we’ve had to inject human behavior into the process of gathering data and looking at data. Using predictive analytics for lifecycle management of equipment is much easier—using manual processes doesn’t provide the predictive piece. It can take two to three weeks to analyze data from these devices, and so automating the gathering and analysis of this data can help you predict what to do and be ready for future events.”
Glassbeam’s analytics and data collection runs on Amazon’s cloud services, says Puneet Pandit, the company’s CEO. Service logs from imaging devices are extensive but often result in vast quantities of unstructured data that contains a lot of semantic meaning. Digesting the output of these devices can help improve service and provide better care to patients, he says.
Time savings in managing complex imaging devices can be significant, Beltre says. Predicting part failure or wise use of preventive maintenance can result in huge time savings. If a part fails in an imaging device, it can take 40 hours to obtain the replacement, install it and test it, he says. Getting ahead of part failure can increase device uptime and reduce costs for procuring replacement parts, he says.
Founded in 2010 as an online loyalty card service, Punchh has since grown into a marketing platform serving more than 115 restaurant chains, including Pizza Hut and Quiznos. Now it’s raised a $20 million Series B to expand into more retail verticals and increase the use of artificial intelligence and machine learning in its cloud software. The funding was led by Sapphire Ventures, with participation from returning investor Cervin Ventures.
Along with its angel and Series A financing, this brings Punchh’s total funding so far to about $31 million. The startup says its goal is to give brick-and-mortar stores the same level of data analytics as e-commerce giants like Amazon.
Punchh’s platform enables restaurants to digitize their customer loyalty programs and complements that with tools like Punchh Acquire, which is designed to help businesses turn casual customers into regulars by promoting offers through multiple channels, including email, SMS, social media, Apple Pay and eClub.
The company currently has 145 employees and is based in San Mateo, California, with offices in Austin, Texas and Delhi. This is Punchh’s first funding announcement in three years and the startup’s largest round of financing by far (it raised $9.5 million Series A in 2015).
Co-founder and chief executive officer Shyam Rao says the time was right for Punchh to raise again because it already serves many of the biggest restaurant chains, with 34,000 locations between them, and wanted to tap into demand from retailers in other verticals.
Punchh is now focusing on convenience stores, gas stations and health and beauty brands (clients already include Fantastic Sams hair salons and TruFusion, a chain of fitness studios). The company competes with other digital loyalty and marketing platforms like Stamp Me, LoyalZoo and Stocard. Rao says Punchh’s ability to create campaigns that target a very specific audience sets it apart from rivals. Punchh’s algorithms pulls together data from several sources, including event calendars, weather, local demographics and the purchasing history of individual customers, for what it describes as “micro-moment marketing.”
For example, if cold weather is expected over a holiday weekend, it might send offers for a discounted hot soup and tea set to mothers between the ages of 30 to 55. Punchh claims it increases spending at its customers’ restaurants by 10% to 20%.
“Imagine trying to manage that process of using mountains of data to build customer relationships and tailor every experience, at scale across hundreds of locations. That’s what Punchh does,” says Rao.
In a statement, Jai Das, Sapphire Ventures managing director said “Punchh is already a global leader in digital marketing solutions for restaurants, which alone would be a fantastic reason to invest in the company, but the scope of their technology goes far beyond just restaurants and encompasses all brick-and-mortar stores with a POS.”
SANTA CLARA, Calif., May 31, 2018 /PRNewswire/ -- Glassbeam, Inc., the premier machine data analytics company focused on supporting medical device OEMs, independent service organizations (ISOs) and healthcare delivery organizations, announced today that Calamed LLC has become a strategic reseller of Glassbeam solutions. Calamed is a leading ISO in the Americas focused on the Caribbean and Latin America healthcare market. With this new partnership, Calamed gains the competitive advantage to deploy advanced analytics on complex machine data collected from MRI machines and CT scanners as well as provide remote monitoring services to ensure maximum equipment uptime.
"Medical imaging machines such as MRI and CT scanners play a critical role in modern healthcare where downtime of these machines creates a double negative of lost revenues and patient dissatisfaction," said Carlos Borges, Managing Director, Calamed LLC. "Predicting anomalous behavior and proactively alerting field engineers through advanced analytics solutions such as Glassbeam is a game changer for the industry."
Glassbeam will exhibit its solution offerings at the Association for the Advancement of Medical Instrumentation (AAMI) 2018 Conference and Expo, to be held June 1-4 at the Long Beach Convention Center, Long Beach, California, booth 170.
Glassbeam also announced expansion of its Artificial Intelligence (AI) solution with Anomaly Detection models for imaging modalities. Generally, advanced healthcare equipment, such as MRI machines and CT scanners, include a combination of sophisticated hardware and software systems that constantly generate valuable machine data signals in complex log formats. For example, a CT scanner logs information every minute on its tube temperature, water temperature, fan speed, air temperature, waterflow and many other variables. Glassbeam uses AI to predict anomalies on these data sets, thus empowering organizations to save millions of dollars in maintenance cost and make strategic data driven decisions.
In its new report, "FDA Report on the Quality, Safety, and Effectiveness of Servicing of Medical Devices," the FDA references the value machine data and analytics provides to optimizing patient safety and operational efficiency by delivering high-quality, safe and effective servicing of medical devices. "FDA finds, as a result of reviewing service records, that the data resulting form the maintenance and repair of medical devices provide valuable insight into the adequacy of the performance of devices." Glassbeam can increase machine uptime from an industry standard of 96-97 percent to more than 99.5 percent. This difference can save healthcare organizations millions of dollars per year.
"Glassbeam analyzes approximately 18 billion events across multiple healthcare machines with about 100 million events per day and 50,000 events per system per day," said Puneet Pandit, Co-founder and CEO at Glassbeam. "This information gold mine helps us build advanced analytics with new machine learning models that provide our customers with advanced warnings. These alerts save millions of dollars in revenue leakage and improve machine uptime across their entire range of equipment."
Glassbeam is the premier machine data analytics company bringing structure and meaning to complex data generated from any connected machine in the Industrial IoT industry with a strong focus on medical and data center equipment. Funded by several ultra-high net worth investors, Glassbeam's next generation cloud-based platform is designed to transform, analyze, and build Artificial Intelligence applications from multi-structured logs, delivering powerful solutions on customer support and product intelligence for companies such as IBM, Dell EMC, PTC, Novant Health, UCSF Health, and Dimension Data. For more information visit http://www.glassbeam.com or follow us on Twitter @Glassbeam.
Mon04Jun2018by John R. Fischer , Staff Reporter
Maintenance and repair for CT scanners may soon be more immediate, less frequent and more affordable following the upcoming expansion of Glassbeam Inc.’s anomaly detection technology.
The machine data analytics company elaborated on the development at the AAMI 2018 Conference and Expo in Long Beach, California, referring to it as a part of its approach for utilizing AI capabilities to detect and alert providers to changes in components of computed tomography scanners from tube temperature to waterflow. They plan to eventually include other critical imaging modalities such as MR.
“Instead of a human being saying that the temperature pressure has shot beyond portable range, the machine alerts you by looking up the historical data of the temperature reading and saying the temperature should be between this high range and this low range. That is the anomaly direction model,” Puneet Pandit, president and CEO of Glassbeam, told HCB News. “The machine will look at the historical data, create the threshold and then alert the engineers when the threshold is crossed.”
CT scanners are equipped with sensors for monitoring different variables such as water temperature, waterflow, air temperature, fan speed, and tube temperature. Though each sensor periodically records its readings to determine if tracked variables are in the normal range, the task of accurately identifying which sensor readings are in the normal range and which ones are not is complex, often leading many to use a rule of thumb to form manually-defined thresholds.
ML-based AD techniques use historical data to train a model that can be used for detecting anomalous sensor values.
With Glassbeam’s technology, providers can utilize machine learning-based AD techniques to predict anomalies from historical data sets and address issues earlier, saving millions in maintenance costs, as well as being able to plan out more efficiently strategic actions for the management of their imaging modalities.
In addition to detecting single abnormal readings, the technology may be used to detect combinations of these readings from two or more different sensors, further helping Glassbeam raise mean time between failures and machine uptime from the industry standard range of 96-97 percent to more than 99.5 percent.
The expansion is the second phase of an initiative launched in February in which machine learning was deployed to detect with high accuracy tube failure in CTs, seven to ten days prior to the actual occurrence of such events.
It also follows the recent partnerships established with Brown's Medical Imaging, Radiographic Equipment Services, and Calamed, which along with all of Glassbeam’s other strategic partners will distribute the solution to providers.
Pandit says the introduction of these capabilities signify the direction that all players in the medical equipment industry should be looking toward as they are are necessary for strengthening the efficiency of connectivity among medical devices and the makeup of the Internet of Things.“This is the time for somebody to come forward and tell the hospital owners that you can collect this operational data from these connected machines, and do these five use cases to become more proactive and predictive and save money, and increase revenue and reclaim revenues,” he said. “Every machine is becoming increasingly connected. Data is there. The value of analytics is a lot higher today because there’s no more extra work to be done. The work is being done in the cloud today and can be applied to any machine or hospital tomorrow.”
Support for these capabilities is backed by the FDA in its new report on the quality, safety and effectiveness of medical devices, finding that data retrieved from maintenance and repair of devices provides “valuable insight” into how well they perform.
The expansion is expected to go live in July or August.
SANTA CLARA, Calif. – AUG 16, 2018 – Glassbeam, Inc., the premier machine data analytics company, has been awarded a 2018 IoT Evolution Product of the Year Award for their healthcare solution from IoT Evolution magazine and IoT Evolution World, the leading magazine and website for IoT technologies news.
Healthcare equipment manufacturers and providers have embraced the Internet of Things (IoT), connecting machines to networks to enable analytics and draw insights on better support and utilization metrics. According to several sources, global healthcare expenditures expanded to over $8 trillion in 2016; capital expenditures for machines, devices and equipment totaled over $350 billion. However, most machine data management tools can only analyze simple sensor data and basic historical log data.
“Today’s tools are only scratching the surface of unearthing the value of complex machine data,” said Puneet Pandit, Founder and CEO of Glassbeam. “Healthcare organizations are aggressively looking for ways to increase revenues, reduce costs and improve patient care. Glassbeam is unlocking the wealth of knowledge that can be extracted from these IoT devices to drive the healthcare industry forward.”
“The solutions selected for the IoT Evolution Product of Year Award reflect the diverse range of innovation driving the multi-billion dollar IoT market today. It is my honor to congratulate Glassbeam for their innovative work and superior contribution to the rapidly evolving IoT industry,” said Carl Ford, CEO of Crossfire Media, a co-publisher of IoT Evolution.
“It is my pleasure to recognize Glassbeam for Healthcare, an innovative solution that earned Glassbeam the 2018 IoT Evolution Product of the Year Award,” said Rich Tehrani, CEO, TMC. “I look forward to seeing more innovation from Glassbeam in the future.”
Glassbeam is the premier machine data analytics company bringing structure and meaning to complex data generated from any connected machine in the Industrial IoT industry with a strong focus on medical and data center equipment. Funded by several ultra-high net worth investors, Glassbeam’s next generation cloud-based platform is designed to transform, analyze, and build Artificial Intelligence applications from multi-structured logs, delivering powerful solutions on customer support and product intelligence for companies such as IBM, Dell EMC, PTC, Novant Health, UCSF Health, and Dimension Data. For more information visit http://www.glassbeam.com or follow us on Twitter @Glassbeam.
About Crossfire Media
Crossfire Media is an integrated marketing company with a core focus on future trends in technology. We service communities of interest with conferences, tradeshows, webinars and newsletters. Crossfire Media has a partnership with Technology Marketing Corporation (TMC) to produce events and websites related to disruptive technologies. Crossfire Media is a division of Crossfire Consulting, a full-service Information Technology company based in New York.
Through education, industry news, live events and social influence, global buyers rely on TMC’s content-driven marketplaces to make purchase decisions and navigate markets. As a result, leading technology vendors turn to TMC for unparalleled branding, thought leadership and lead generation opportunities. Our in-person and online events deliver unmatched visibility and sales prospects for all percipients. Through our custom lead generation programs, we provide clients with an ongoing stream of leads that turn into sales opportunities and build databases. Additionally, we bolster brand reputations with the millions of impressions from display advertising on our news sites and newsletters. Making TMC a 360 degree marketing solution, we offer comprehensive event and road show management services and custom content creation with expertly ghost-crafted blogs, press releases, articles and marketing collateral to help with SEO, branding, and overall marketing efforts. For more information about TMC and to learn how we can help you reach your marketing goals, please visit www.tmcnet.com and follow us on Facebook, LinkedIn and Twitter, @tmcnet.
On the heels of a great event and presentation along with Rick Gaylord, our healthcare solution specialist, at the 2018 CEAI Conference, I want to continue the conversation about the far-reaching impacts of machine data and artificial intelligence for healthcare technology.
Machine data from IoT connected devices is growing at 50 times the growth rate of traditional business data. By 2025, more than 42 percent of the world’s data will be machine-generated.
There are massive amounts of machine data, hidden from normal view, in unstructured, complex and messy formats. With the right specialized tools, this data can be cleaned up, organized and is ideal for interpretation through machine learning and predictive analytics. Machine data can be transformed into insights that can drive automation, optimize utilization, diagnose and prevent equipment issues, and save organizations millions of dollars in increased efficiencies and decreased equipment failures.
Machine data has the power to transform major industries, including oil and gas, power, aviation, rail, and of course, healthcare. In fact, efficiency gains from IoT and machine data – including automated diagnostics, remote patient monitoring, and performance monitoring – could have a $63 billion business impact in healthcare. Some of the medical machines that generate the amount of data include MRI machines, CT scanners, x-ray machines, defibrillators, ultrasound equipment, ventilators, patient monitoring systems, anesthesia machines, and many more.
Key Use Cases of Machine Data
Out of the vast medical devices that generate machine data, a few of the top use cases include:
- Machine Utilization – Machine data can help organizations understand the number of procedures per machine, per facility, by manufacturer type, to optimize equipment utilization and budgets.
- MRI Machine Health – Analyzing machine data can alert operators of key triggers and send proactive maintenance alerts.
- CT Scanner Health – Predictive analytics allows for real-time maintenance to avoid equipment and system failures and downtime.
- Environmental Sensors – Monitoring environmental variables can indicate when key triggers might lead to equipment failure and send proactive alerts to avoid downtime.
- Operator Usage & Analytics – Machine analytics offers insight into the behavior of equipment operators and can help identify and address gaps in training and balance load.
For more on Machine Log Data Use Cases: Data Doesn’t Lie: 5 Ways Hospitals Can Use Machine Log Data
Machine Data, Artificial Intelligence, and Machine Learning
Consider the massive amount of log data that even a single system produces. More than 50,000 events are logged each day by each system, with more than 2,500 different types of warning and error events. Machine learning (ML) and artificial intelligence (AI) allows us to process and interpret machine log data that might otherwise be too complex or simply too time-consuming for the human mind to analyze effectively.
With ML and AI-enabled predictive maintenance, organizations can avoid unplanned reactive maintenance and downtime, plan preventative maintenance during non-business hours, and even set up custom alerts and rules about equipment maintenance notifications. The possibilities are virtually endless.
The business impact of predictive analytics is quite impressive. On average, an expensive imaging machine like an MRI or CT scanner will face an issue eight to ten times per year and will be down six to eight hours each time – equating to about 62 hours of downtime, per machine, per year.
A facility with five MRI machines and five CT scanners that uses predictive analytics and maintenance can perform 500 additional procedures per year and earn $3 million in additional revenues over 3 years. This is at an assumed operating schedule of 10 hours per day, six days per week, and one procedure per hour at $2,000 per procedure.
More on business impact: Machine Learning and Predictive Maintenance Maximizes Healthcare ROI
Who Owns the Data?
With all of the machine data out there, one of the most pressing questions circulating in clinical engineering and healthcare technology communities is this – who owns it? Is it the Original Equipment Manufacturers (OEMs) who make the equipment, Independent Sales Organizations (ISOs) who re-sell the equipment, or healthcare providers who operate it?
Among other reasons, OEMs often believe they own the data because they own the software that generates the data. ISOs believe they need access to the machine data to better service customers. Providers believe that they have a right to the data because they paid for the machines. They are all valid points, and we will continue to expand on them in upcoming initiatives and content on data ownership. Stay tuned!
SANTA CLARA, Calif. – September 26, 2018 – Glassbeam, Inc., the premier machine data analytics company, announced today an expansion of its Clinical Engineering Analytics (CLEAN™) offerings to become the only AI analytics solution to combine machine logs with DICOM data. The company also announced its solution now includes biomedical equipment as well as imaging systems. Glassbeam enables healthcare provider teams to manage entire fleets of medical equipment in a single view.
The company has completed a third party assessment to verify Glassbeam’s compliance with the HIPAA Privacy, Security, and Breach Notification Rules and ongoing commitment to protect individually identifiable health information collected by its advanced data analytics solution. HIPAA, the Health Insurance Portability and Accountability Act of 1996, applies to healthcare providers, insurers, hospitals, and any company with access to patients’ protected health information (PHI) – including Glassbeam, which provides advanced data analytics services to healthcare organizations.
Connected medical devices transmit errors, warnings, and invaluable utilization data that when translated, provide healthcare organizations a clear picture of equipment performance, usage, and overall operational effectiveness. The challenge is that this data comes from different data sources with multiple formats, such as machine logs and DICOM headers when images are transmitted to PACS servers. Users of Glassbeam’s machine data analytics solution can now uncover deep insights by assimilating all of these data sources into one platform to dramatically improve uptime and maximize revenue of medical equipment while safeguarding patients’ PHI.
The expansion of Glassbeam’s data analytics to include biomedical devices, such as GE CARESCAPE patient monitors and Medfusion Syringe Pumps, has been another breakthrough in its cloud-based analytics offerings. Large hospitals and Integrated Delivery Networks (IDNs) have hundreds to thousands of biomedical devices in their facilities. It can be a challenge to track the actual utilization of devices and preventative maintenance schedules involve costly labor.
“Our vision at Glassbeam has from day one been to be a pioneer in providing a single pane of glass for multi-vendor, multi-modality analytics for healthcare provider market,” said Puneet Pandit, CEO and co-founder of Glassbeam. “Not only can Glassbeam now offer an expanded view into the performance of entire fleets of medical equipment, whether imaging or biomedical devices, but we can now offer complete confidence that if patient data is pulled into Glassbeam Cloud, it will be highly secure.”
Online Business Systems provided Glassbeam’s assessment of HIPAA Compliance, following the comprehensive rules and regulations laid out by the U.S. Department of Health and Human Services (HHS), the government body responsible for regulating and enforcing HIPAA.
“Protection of patient information is our number one priority in the healthcare industry,” said Steve Levinson, Vice President of Risk, Security and Privacy at Online Business Systems. “We are proud to see that Glassbeam is taking the protection of that data so seriously. We are confident that as the solution integrates with new data sources, all patient data will remain secure to the highest standards.”
Glassbeam will be exhibiting at the MD Expo show in Seattle on Oct. 5-7. Stop by booth #715 for a demo or schedule a meeting to discuss how Glassbeam can maximize equipment utilization, performance, and revenues.
Glassbeam is the premier machine data analytics company bringing structure and meaning to complex data generated from any connected machine in the Industrial IoT industry with a strong focus on medical and data center equipment. Funded by several ultra-high net worth investors, Glassbeam’s next generation cloud-based platform is designed to transform, analyze, and build Artificial Intelligence applications from multi-structured logs, delivering powerful solutions on customer support and product intelligence for companies such as IBM, Dell EMC, PTC, Novant Health, UCSF Health, and Dimension Data. For more
Public Relations for Glassbeam, Inc.
Wed26Sep2018Wednesday, September 26, 2018
SANTA CLARA, Calif. – September 26, 2018 – Glassbeam, Inc., the premier machine data analytics company, announced today that NIR (National Imaging Resources) has selected Glassbeam to expand its Equipment Service Partnership (ESP) Asset Management program and drive innovation in their multivendor service organization. As part of the agreement, NIR will resell, market, maintain and collaborate on further development of the Glassbeam Artificial Intelligence, Predictive Analytic software for imaging, x-ray and biomedical equipment.
NIR is an independent medical imaging and clinical equipment sales and service organization owned and operated by seasoned entrepreneurs that have more than 470 years of combined experience in the healthcare industry. Through its 16 affiliate partners, operating out of 22 offices across the country, with 83 sales professionals and 115 highly trained field service engineers, NIR sells and services diagnostic imaging and biomedical equipment to provide multivendor service on a variety of imaging systems and clinical equipment.
“NIR, while working with a wide range of diagnostic imaging, x-ray, clinical and bio medical equipment manufactured by different OEMs, will now be able to leverage Glassbeam’s Artificial Intelligence and Predictive Analytic software to give us the analytics edge and allow us to compare performance among different manufacturers to establish ‘best of breed’ performance standards,” said Karl Wolcott, President, National Imaging Resources. “These capabilities will enable us to become a much deeper strategic partner with our customers and help them reduce costs, improve utilization and enhance clinical outcomes.”
“Healthcare providers are aggressively looking for ways to increase revenues, reduce costs and improve patient care, all within a budget-constrained operating and capex environment, said Puneet Pandit, Co-founder and CEO of Glassbeam. “Glassbeam can increase machine uptime from an industry standard of 96-97 percent to more than 99.5 percent. This difference can save healthcare organizations millions of dollars per year. Our partnership with NIR will also help Glassbeam deepen our solution by tapping into their extensive domain expertise and help institutionalize a strong machine data analytics foundation for healthcare providers across the country.”
Glassbeam will be exhibiting at the MD Expo show in Seattle on Oct. 5-7. Stop by booth #715 for a demo or schedule a meeting to discuss how Glassbeam can maximize equipment utilization, performance, and revenues.
Glassbeam is the premier machine data analytics company bringing structure and meaning to complex data generated from any connected machine in the Industrial IoT industry with a strong focus on medical and data center equipment. Funded by several ultra-high net worth investors, Glassbeam’s next generation cloud-based platform is designed to transform, analyze, and build Artificial Intelligence applications from multi-structured logs, delivering powerful solutions on customer support and product intelligence for companies such as IBM, Dell EMC, PTC, Novant Health, UCSF Health, and Dimension Data. For more information visit http://www.glassbeam.com or follow us on LinkedIn or Twitter @Glassbeam.
About National Imaging Resources
National Imaging Resources is an independent medical imaging and clinical equipment sales and service organization. NIR is owned and operated by seasoned entrepreneurs with extensive experience in the healthcare industry. Through its sixteen affiliate partners and 352 employees, operating out of their twenty-two offices located across the country, National Imaging Resources sells and services a wide variety of diagnostic imaging and biomedical equipment for healthcare providers throughout the United States. For more information, visit https://www.nirmedical.com/
Public Relations for Glassbeam, Inc.